{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T14:49:55Z","timestamp":1784299795464,"version":"3.55.0"},"publisher-location":"Cham","reference-count":83,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031732461","type":"print"},{"value":"9783031732478","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73247-8_17","type":"book-chapter","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T12:02:20Z","timestamp":1730376140000},"page":"285-303","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["XPSR: Cross-Modal Priors for\u00a0Diffusion-Based Image Super-Resolution"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9700-6290","authenticated-orcid":false,"given":"Yunpeng","family":"Qu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3681-2196","authenticated-orcid":false,"given":"Kun","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5237-7512","authenticated-orcid":false,"given":"Kai","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6171-9789","authenticated-orcid":false,"given":"Qizhi","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhua","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"17_CR1","unstructured":"Achiam, J., et\u00a0al.: GPT-4 Technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: dataset and study. In: CVPR Workshops, pp. 1122\u20131131. IEEE Computer Society (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"17_CR3","unstructured":"Bell-Kligler, S., Shocher, A., Irani, M.: Blind super-resolution kernel estimation using an internal-GAN. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR, pp. 10674\u201310685. IEEE (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Brooks, T., Holynski, A., Efros, A.A.: Instructpix2pix: learning to follow image editing instructions. In: CVPR, pp. 18392\u201318402. IEEE (2023)","DOI":"10.1109\/CVPR52729.2023.01764"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Chan, K.C.K., Wang, X., Xu, X., Gu, J., Loy, C.C.: GLEAN: generative latent bank for large-factor image super-resolution. In: CVPR, pp. 14245\u201314254. Computer Vision Foundation \/ IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.01402"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Chen, C., et al.: Real-world blind super-resolution via feature matching with implicit high-resolution priors. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 1329\u20131338 (2022)","DOI":"10.1145\/3503161.3547833"},{"key":"17_CR8","unstructured":"Chen, Z., et al.: Image super-resolution with text prompt diffusion. CoRR abs\/2311.14282 (2023)"},{"key":"17_CR9","unstructured":"Cheng, J., et al.: Black-box prompt optimization: aligning large language models without model training. arXiv preprint arXiv:2311.04155 (2023)"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Dai, T., Cai, J., Zhang, Y., Xia, S.T., Zhang, L.: Second-order attention network for single image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11065\u201311074 (2019)","DOI":"10.1109\/CVPR.2019.01132"},{"issue":"5","key":"17_CR11","first-page":"2567","volume":"44","author":"K Ding","year":"2022","unstructured":"Ding, K., Ma, K., Wang, S., Simoncelli, E.P.: Image quality assessment: unifying structure and texture similarity. IEEE Trans. Pattern Anal. Mach. Intell. 44(5), 2567\u20132581 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"17_CR12","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-319-46475-6_25","volume-title":"Computer Vision \u2013 ECCV 2016","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391\u2013407. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_25"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Fei, B., et al.: Generative diffusion prior for unified image restoration and enhancement. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, 17\u201324 June 2023, pp. 9935\u20139946 (2023)","DOI":"10.1109\/CVPR52729.2023.00958"},{"key":"17_CR15","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1023\/A:1026501619075","volume":"40","author":"WT Freeman","year":"2000","unstructured":"Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning low-level vision. Int. J. Comput. Vis. 40, 25\u201347 (2000)","journal-title":"Int. J. Comput. Vis."},{"key":"17_CR16","unstructured":"Gao, P., et\u00a0al.: Llama-adapter v2: parameter-efficient visual instruction model. arXiv preprint arXiv:2304.15010 (2023)"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Gu, J., Lu, H., Zuo, W., Dong, C.: Blind super-resolution with iterative kernel correction. In: CVPR, pp. 1604\u20131613. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.00170"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Gu, J., Shen, Y., Zhou, B.: Image processing using multi-code GAN prior. In: CVPR, pp. 3009\u20133018. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00308"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Gu, S., Lugmayr, A., Danelljan, M., Fritsche, M., Lamour, J., Timofte, R.: DIV8K: diverse 8k resolution image dataset. In: ICCV Workshops, pp. 3512\u20133516. IEEE (2019)","DOI":"10.1109\/ICCVW.2019.00435"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778. IEEE Computer Society (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"17_CR21","unstructured":"Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., Cohen-Or, D.: Prompt-to-prompt image editing with cross-attention control. In: ICLR. OpenReview.net (2023)"},{"key":"17_CR22","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: NIPS, pp. 6626\u20136637 (2017)"},{"key":"17_CR23","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"17_CR24","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: NeurIPS (2020)"},{"key":"17_CR25","unstructured":"Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)"},{"key":"17_CR26","doi-asserted-by":"publisher","first-page":"4041","DOI":"10.1109\/TIP.2020.2967829","volume":"29","author":"V Hosu","year":"2020","unstructured":"Hosu, V., Lin, H., Szir\u00e1nyi, T., Saupe, D.: Koniq-10k: an ecologically valid database for deep learning of blind image quality assessment. IEEE Trans. Image Process. 29, 4041\u20134056 (2020)","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"17_CR27","doi-asserted-by":"publisher","first-page":"4217","DOI":"10.1109\/TPAMI.2020.2970919","volume":"43","author":"T Karras","year":"2021","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. IEEE Trans. Pattern Anal. Mach. Intell. 43(12), 4217\u20134228 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR28","unstructured":"Kawar, B., Elad, M., Ermon, S., Song, J.: Denoising diffusion restoration models. In: NeurIPS (2022)"},{"key":"17_CR29","first-page":"23593","volume":"35","author":"B Kawar","year":"2022","unstructured":"Kawar, B., Elad, M., Ermon, S., Song, J.: Denoising diffusion restoration models. Adv. Neural Inf. Process. Syst. 35, 23593\u201323606 (2022)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"17_CR30","doi-asserted-by":"crossref","unstructured":"Kawar, B., et al.: Imagic: text-based real image editing with diffusion models. In: CVPR, pp. 6007\u20136017. IEEE (2023)","DOI":"10.1109\/CVPR52729.2023.00582"},{"key":"17_CR31","doi-asserted-by":"crossref","unstructured":"Ke, J., Wang, Q., Wang, Y., Milanfar, P., Yang, F.: Musiq: multi-scale image quality transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5148\u20135157 (2021)","DOI":"10.1109\/ICCV48922.2021.00510"},{"key":"17_CR32","doi-asserted-by":"crossref","unstructured":"Kim, Y., Son, D.: Noise conditional flow model for learning the super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)","DOI":"10.1109\/CVPRW53098.2021.00053"},{"key":"17_CR33","unstructured":"Li, J., Li, D., Savarese, S., Hoi, S.: Blip-2: bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597 (2023)"},{"key":"17_CR34","unstructured":"Li, J., Li, D., Xiong, C., Hoi, S.C.H.: BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation. In: ICML. Proceedings of Machine Learning Research, vol.\u00a0162, pp. 12888\u201312900. PMLR (2022)"},{"key":"17_CR35","doi-asserted-by":"crossref","unstructured":"Li, W., Zhou, K., Qi, L., Lu, L., Lu, J.: Best-buddy GANs for highly detailed image super-resolution. In: AAAI, pp. 1412\u20131420. AAAI Press (2022)","DOI":"10.1609\/aaai.v36i2.20030"},{"key":"17_CR36","doi-asserted-by":"crossref","unstructured":"Liang, J., Zeng, H., Zhang, L.: Details or artifacts: a locally discriminative learning approach to realistic image super-resolution. In: CVPR, pp. 5647\u20135656. IEEE (2022)","DOI":"10.1109\/CVPR52688.2022.00557"},{"key":"17_CR37","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Gool, L.V., Timofte, R.: Swinir: image restoration using swin transformer. In: ICCVW, pp. 1833\u20131844. IEEE (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"17_CR38","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: CVPR Workshops, pp. 1132\u20131140. IEEE Computer Society (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"17_CR39","unstructured":"Lin, X., et al.: Diffbir: towards blind image restoration with generative diffusion prior. CoRR abs\/2308.15070 (2023)"},{"key":"17_CR40","unstructured":"Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. Adv. Neural Inf. Process. Syst. 36 (2024)"},{"key":"17_CR41","doi-asserted-by":"crossref","unstructured":"Liu, H., et al.: Ada-dqa: adaptive diverse quality-aware feature acquisition for video quality assessment. In: ACM Multimedia, pp. 6695\u20136704. ACM (2023)","DOI":"10.1145\/3581783.3611795"},{"key":"17_CR42","unstructured":"Loshchilov, I., Hutter, F.: Fixing weight decay regularization in adam (2017)"},{"key":"17_CR43","unstructured":"Luo, F., Wu, X., Guo, Y.: And: adversarial neural degradation for learning blind image super-resolution. Adv. Neural Inf. Process. Syst. 36 (2024)"},{"key":"17_CR44","doi-asserted-by":"crossref","unstructured":"Menon, S., Damian, A., Hu, S., Ravi, N., Rudin, C.: PULSE: self-supervised photo upsampling via latent space exploration of generative models. In: CVPR, pp. 2434\u20132442. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00251"},{"key":"17_CR45","doi-asserted-by":"crossref","unstructured":"Michaeli, T., Irani, M.: Nonparametric blind super-resolution. In: ICCV, pp. 945\u2013952. IEEE Computer Society (2013)","DOI":"10.1109\/ICCV.2013.121"},{"issue":"12","key":"17_CR46","doi-asserted-by":"publisher","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","volume":"21","author":"A Mittal","year":"2012","unstructured":"Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695\u20134708 (2012)","journal-title":"IEEE Trans. Image Process."},{"key":"17_CR47","doi-asserted-by":"crossref","unstructured":"Mou, C., et al.: T2i-adapter: learning adapters to dig out more controllable ability for text-to-image diffusion models. CoRR abs\/2302.08453 (2023)","DOI":"10.1609\/aaai.v38i5.28226"},{"key":"17_CR48","unstructured":"Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: ICML. Proceedings of Machine Learning Research, vol.\u00a0139, pp. 8162\u20138171. PMLR (2021)"},{"key":"17_CR49","unstructured":"OpenAI: Gpt-4v(ision) system card (2023). https:\/\/openai.com\/research\/gpt-4v-system-card"},{"key":"17_CR50","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"17_CR51","unstructured":"Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.061251(2), 3 (2022)"},{"key":"17_CR52","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"17_CR53","unstructured":"Sahak, H., Watson, D., Saharia, C., Fleet, D.: Denoising diffusion probabilistic models for robust image super-resolution in the wild. arXiv preprint arXiv:2302.07864 (2023)"},{"key":"17_CR54","unstructured":"Saharia, C., et al.: Photorealistic text-to-image diffusion models with deep language understanding. Adv. Neural Inf. Process. Syst. 35, 36479\u201336494 (2022)"},{"issue":"4","key":"17_CR55","first-page":"4713","volume":"45","author":"C Saharia","year":"2022","unstructured":"Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, M.: Image super-resolution via iterative refinement. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4713\u20134726 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"17_CR56","first-page":"4713","volume":"45","author":"C Saharia","year":"2023","unstructured":"Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, M.: Image super-resolution via iterative refinement. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4713\u20134726 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"17_CR57","doi-asserted-by":"publisher","first-page":"3440","DOI":"10.1109\/TIP.2006.881959","volume":"15","author":"HR Sheikh","year":"2006","unstructured":"Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15(11), 3440\u20133451 (2006)","journal-title":"IEEE Trans. Image Process."},{"key":"17_CR58","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van\u00a0Gool, L., Yang, M.H., Zhang, L.: Ntire 2017 challenge on single image super-resolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 114\u2013125 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"17_CR59","unstructured":"Touvron, H., et\u00a0al.: Llama: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)"},{"key":"17_CR60","doi-asserted-by":"crossref","unstructured":"Wang, J., Chan, K.C., Loy, C.C.: Exploring clip for assessing the look and feel of images. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 2555\u20132563 (2023)","DOI":"10.1609\/aaai.v37i2.25353"},{"key":"17_CR61","doi-asserted-by":"crossref","unstructured":"Wang, J., Yue, Z., Zhou, S., Chan, K.C.K., Loy, C.C.: Exploiting diffusion prior for real-world image super-resolution. CoRR abs\/2305.07015 (2023)","DOI":"10.1007\/s11263-024-02168-7"},{"key":"17_CR62","doi-asserted-by":"crossref","unstructured":"Wang, J., Yue, Z., Zhou, S., Chan, K.C., Loy, C.C.: Exploiting diffusion prior for real-world image super-resolution. arXiv preprint arXiv:2305.07015 (2023)","DOI":"10.1007\/s11263-024-02168-7"},{"key":"17_CR63","doi-asserted-by":"crossref","unstructured":"Wang, X., Xie, L., Dong, C., Shan, Y.: Real-esrgan: training real-world blind super-resolution with pure synthetic data. In: ICCVW, pp. 1905\u20131914. IEEE (2021)","DOI":"10.1109\/ICCVW54120.2021.00217"},{"key":"17_CR64","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Dong, C., Loy, C.C.: Recovering realistic texture in image super-resolution by deep spatial feature transform. In: CVPR, pp. 606\u2013615. Computer Vision Foundation\/IEEE Computer Society (2018)","DOI":"10.1109\/CVPR.2018.00070"},{"issue":"4","key":"17_CR65","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"17_CR66","unstructured":"Wu, H., et\u00a0al.: Q-bench: a benchmark for general-purpose foundation models on low-level vision. arXiv preprint arXiv:2309.14181 (2023)"},{"key":"17_CR67","unstructured":"Wu, R., Yang, T., Sun, L., Zhang, Z., Li, S., Zhang, L.: Seesr: towards semantics-aware real-world image super-resolution. CoRR abs\/2311.16518 (2023)"},{"key":"17_CR68","doi-asserted-by":"crossref","unstructured":"Yang, S., et al.: MANIQA: multi-dimension attention network for no-reference image quality assessment. In: CVPR Workshops, pp. 1190\u20131199. IEEE (2022)","DOI":"10.1109\/CVPRW56347.2022.00126"},{"key":"17_CR69","doi-asserted-by":"crossref","unstructured":"Yang, T., Ren, P., Xie, X., Zhang, L.: Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization. arXiv preprint arXiv:2308.14469 (2023)","DOI":"10.1007\/978-3-031-73247-8_5"},{"key":"17_CR70","unstructured":"Yin, S., et al.: A survey on multimodal large language models. arXiv preprint arXiv:2306.13549 (2023)"},{"key":"17_CR71","doi-asserted-by":"crossref","unstructured":"You, Z., Li, Z., Gu, J., Yin, Z., Xue, T., Dong, C.: Depicting beyond scores: advancing image quality assessment through multi-modal language models. arXiv preprint arXiv:2312.08962 (2023)","DOI":"10.1007\/978-3-031-72970-6_15"},{"key":"17_CR72","doi-asserted-by":"crossref","unstructured":"Yuan, K., Kong, Z., Zheng, C., Sun, M., Wen, X.: Capturing co-existing distortions in user-generated content for no-reference video quality assessment. In: ACM Multimedia, pp. 1098\u20131107. ACM (2023)","DOI":"10.1145\/3581783.3612023"},{"key":"17_CR73","doi-asserted-by":"crossref","unstructured":"Zhang, K., Liang, J., Gool, L.V., Timofte, R.: Designing a practical degradation model for deep blind image super-resolution. In: ICCV, pp. 4771\u20134780. IEEE (2021)","DOI":"10.1109\/ICCV48922.2021.00475"},{"key":"17_CR74","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3262\u20133271 (2018)","DOI":"10.1109\/CVPR.2018.00344"},{"issue":"8","key":"17_CR75","doi-asserted-by":"publisher","first-page":"2579","DOI":"10.1109\/TIP.2015.2426416","volume":"24","author":"L Zhang","year":"2015","unstructured":"Zhang, L., Zhang, L., Bovik, A.C.: A feature-enriched completely blind image quality evaluator. IEEE Trans. Image Process. 24(8), 2579\u20132591 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"17_CR76","doi-asserted-by":"crossref","unstructured":"Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3836\u20133847 (2023)","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"17_CR77","unstructured":"Zhang, P., et\u00a0al.: Internlm-xcomposer: a vision-language large model for advanced text-image comprehension and composition. arXiv preprint arXiv:2309.15112 (2023)"},{"key":"17_CR78","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR, pp. 586\u2013595. Computer Vision Foundation\/IEEE Computer Society (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"17_CR79","unstructured":"Zhang, R., Gu, J., Chen, H., Dong, C., Zhang, Y., Yang, W.: Crafting training degradation distribution for the accuracy-generalization trade-off in real-world super-resolution. In: ICML. Proceedings of Machine Learning Research, vol.\u00a0202, pp. 41078\u201341091. PMLR (2023)"},{"key":"17_CR80","unstructured":"Zhang, Y., et al.: Recognize anything: a strong image tagging model. CoRR abs\/2306.03514 (2023)"},{"key":"17_CR81","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/978-3-030-01234-2_18","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294\u2013310. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_18"},{"key":"17_CR82","doi-asserted-by":"crossref","unstructured":"Zhao, K., Yuan, K., Sun, M., Li, M., Wen, X.: Quality-aware pre-trained models for blind image quality assessment. In: CVPR, pp. 22302\u201322313 (2023)","DOI":"10.1109\/CVPR52729.2023.02136"},{"key":"17_CR83","unstructured":"Zhu, D., Chen, J., Shen, X., Li, X., Elhoseiny, M.: Minigpt-4: enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592 (2023)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73247-8_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T15:39:01Z","timestamp":1732981141000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73247-8_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"ISBN":["9783031732461","9783031732478"],"references-count":83,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73247-8_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"assertion":[{"value":"1 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}